Multi-omics Characterization of Interaction-mediated Control of Human Protein Abundance levels.

Proteogenomic studies of cancer samples have shown that copy-number variation can be attenuated at the protein level for a large fraction of the proteome, likely due to the degradation of unassembled protein complex subunits. Such interaction-mediated control of protein abundance remains poorly characterized. To study this, we compiled genomic, (phospho)proteomic and structural data for hundreds of cancer samples and find that up to 42% of 8,124 analyzed proteins show signs of post-transcriptional control. We find evidence of interaction-dependent control of protein abundance, correlated with interface size, for 516 protein pairs, with some interactions further controlled by phosphorylation. Finally, these findings in cancer were reflected in variation in protein levels in normal tissues. Importantly, expression differences due to natural genetic variation were increasingly buffered from phenotype differences for highly attenuated proteins. Altogether, this study further highlights the importance of posttranscriptional control of protein abundance in cancer and healthy cells.

Parseq: reconstruction of microbial transcription landscape from RNA-Seq read counts using state-space models.

MOTIVATION: The most common RNA-Seq strategy consists of random shearing, amplification and high-throughput sequencing of the RNA fraction. Methods to analyze transcription level variations along the genome from the read count profiles generated by the RNA-Seq protocol are needed. RESULTS: We developed a statistical approach to estimate the local transcription levels and to identify transcript borders. This transcriptional landscape reconstruction relies on a state-space model to describe transcription level variations in terms of abrupt shifts and more progressive drifts. A new emission model is introduced to capture not only the read count variance inside a transcript but also its short-range autocorrelation and the fraction of positions with zero counts. The estimation relies on a particle Gibbs algorithm whose running time makes it more suited to microbial genomes. The approach outperformed read-overlapping strategies on synthetic and real microbial datasets. AVAILABILITY: A program named Parseq is available at: http://www.lgm.upmc.fr/parseq/. CONTACT: bodgan.mirauta@upmc.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Erosion of human X chromosome inactivation causes major remodeling of the iPSC proteome.

X chromosome inactivation (XCI) is a dosage compensation mechanism in female mammals whereby transcription from one X chromosome is repressed. Analysis of human induced pluripotent stem cells (iPSCs) derived from female donors identified that low levels of XIST RNA correlated strongly with erosion of XCI. Proteomic analysis, RNA sequencing (RNA-seq), and polysome profiling showed that XCI erosion resulted in amplified RNA and protein expression from X-linked genes, providing a proteomic characterization of skewed dosage compensation. Increased protein expression was also detected from autosomal genes without an mRNA increase, thus altering the protein-RNA correlation between the X chromosome and autosomes. XCI-eroded lines display an ∼13% increase in total cell protein content, with increased ribosomal proteins, ribosome biogenesis and translation factors, and polysome levels. We conclude that XCI erosion in iPSCs causes a remodeling of the proteome, affecting the expression of a much wider range of proteins and disease-linked loci than previously realized.

Transcriptome architecture and regulation at environmental transitions in flavobacteria: the case of an important fish pathogen.

The family Flavobacteriaceae (phylum Bacteroidetes) is a major component of soil, marine and freshwater ecosystems. In this understudied family, Flavobacterium psychrophilum is a freshwater pathogen that infects salmonid fish worldwide, with critical environmental and economic impact. Here, we report an extensive transcriptome analysis that established the genome map of transcription start sites and transcribed regions, predicted alternative sigma factor regulons and regulatory RNAs, and documented gene expression profiles across 32 biological conditions mimicking the pathogen life cycle. The results link genes to environmental conditions and phenotypic traits and provide insights into gene regulation, highlighting similarities with better known bacteria and original characteristics linked to the phylogenetic position and the ecological niche of the bacterium. In particular, osmolarity appears as a signal for transition between free-living and within-host programs and expression patterns of secreted proteins shed light on probable virulence factors. Further investigations showed that a newly discovered sRNA widely conserved in the genus, Rfp18, is required for precise expression of proteases. By pointing proteins and regulatory elements probably involved in host-pathogen interactions, metabolic pathways, and molecular machineries, the results suggest many directions for future research; a website is made available to facilitate their use to fill knowledge gaps on flavobacteria.

Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics.

Induced pluripotent stem cells (iPSCs) are an established cellular system to study the impact of genetic variants in derived cell types and developmental contexts. However, in their pluripotent state, the disease impact of genetic variants is less well known. Here, we integrate data from 1,367 human iPSC lines to comprehensively map common and rare regulatory variants in human pluripotent cells. Using this population-scale resource, we report hundreds of new colocalization events for human traits specific to iPSCs, and find increased power to identify rare regulatory variants compared with somatic tissues. Finally, we demonstrate how iPSCs enable the identification of causal genes for rare diseases.

Population-scale proteome variation in human induced pluripotent stem cells.

Human disease phenotypes are driven primarily by alterations in protein expression and/or function. To date, relatively little is known about the variability of the human proteome in populations and how this relates to variability in mRNA expression and to disease loci. Here, we present the first comprehensive proteomic analysis of human induced pluripotent stem cells (iPSC), a key cell type for disease modelling, analysing 202 iPSC lines derived from 151 donors, with integrated transcriptome and genomic sequence data from the same lines. We characterised the major genetic and non-genetic determinants of proteome variation across iPSC lines and assessed key regulatory mechanisms affecting variation in protein abundance. We identified 654 protein quantitative trait loci (pQTLs) in iPSCs, including disease-linked variants in protein-coding sequences and variants with trans regulatory effects. These include pQTL linked to GWAS variants that cannot be detected at the mRNA level, highlighting the utility of dissecting pQTL at peptide level resolution.